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We study preferences estimated from finite choice experiments and provide sufficient conditions for convergence to a unique underlying “true” preference. Our conditions are weak and, therefore, valid in a wide range of economic environments. We develop applications to expected utility theory, choice over consumption bundles, and menu choice. Our framework unifies the revealed preference tradition with models that allow for errors.

The investigation of travel mode choice is an essential task in transport planning and policymaking for predicting travel demands. Typically, mode choice datasets are imbalanced and learning from such datasets is challenging. This study deals with imbalanced mode choice data by developing an algorithm (SVMAK) based on a support vector machine model and the theory of adjusting kernel scaling. The kernel function’s choice was evaluated by applying the likelihood-ratio chi-square and weighting measures. The empirical assessment was performed on the 2017 National Household Travel Survey–California dataset. The performance of the SVMAK model was compared with several other models, including neural networks, XGBoost, Bayesian Network, standard support vector machine model, and some SVM-based models that were previously developed to handle the imbalanced datasets. The SVMAK model outperformed these models, and in some cases improved the accuracy of the minority class classification. For the majority class, the accuracy improvement was substantial. This algorithm can be applied to other tasks in the transport planning domain that deal with uneven data distribution.

Studies have applied single-reference-point or safety margin hypotheses to examine how advanced traveler information affects travel behaviors. However, these theories may fail to fully capture the trade-offs among origin departure time, airport access time, and terminal processing time in terms of airport ground access behaviors. In this study, we developed a tri-reference-point hypothesis and assumed that the rate of change of utility may change at the air passenger’s preferred (PAT), earliest acceptable (EAT), and latest acceptable (LAT) airport arrival times. With an empirical data set collected from 304 passengers at Taipei Songshan Airport, the study examined the tri-reference-point hypothesis by analyzing airport ground access mode choice behaviors with a pooled framework that combined revealed and stated preferences. Moreover, the study developed four alternative specifications for schedule delay variables, assuming that air passengers used different reference points to determine relative gains and losses of the expected airport arrival time. The specifications included selecting both EAT and LAT as the zero-utility points (an indifference-band specification) and either one of PAT, EAT, and LAT as the single zero-utility point. Regardless of which specification was employed for schedule delay variables, the tri-reference-point hypothesis was generally supported. In particular, a significant difference of the rate of change of utility around PAT, EAT, and LAT was identified in the analysis results. When managing increasing road travel times and increasingly congested terminals, air passengers were more willing to retime their origin departure time to an earlier time than to switch their ground access mode. The implications of the analysis results for airport ground access management are discussed in the study.

This article is about the role of factual uncertainty for moral decision-making as it concerns the ethics of machine decision-making (i.e., decisions by AI systems, such as autonomous vehicles, autonomous robots, or decision support systems). The view that is defended here is that factual uncertainties require a normative evaluation and that ethics of machine decision faces a triple-edged problem, which concerns what a machine ought to do, given its technical constraints, what decisional uncertainty is acceptable, and what trade-offs are acceptable to decrease the decisional uncertainty.

The consideration of preference heterogeneity in consumer choice behavior has become state of the art. In addition, the identification of consumer segments remains essential for marketing managers. For disaggregate consumer choice data representing the basis of segmentation, the latent class multinomial logit (MNL) model is currently the most popular approach for estimating segment-specific preferences. After addressing the theoretical background of the latent class MNL model, we use an empirical choice-based conjoint data set to illustrate model estimation and validation, as well as how the estimation results should be interpreted. A particular focus lies on the model selection process, i.e., the determination of an appropriate number of segments. We further work out interpretation pitfalls when the existing preference heterogeneity of consumers is ignored. This will ultimately provide a guide for applying the latent class MNL model regarding model selection, estimation, validation, and interpretation of results both from a statistical and managerial perspective.

In addition to choice questions (revealed and stated choices), preference surveys typically include other questions that provide information about preferences. Preference-statement data include questions on the importance of different attributes of a good or the extent of agreement with a particular statement. The intent of this paper is to model and jointly estimate preference heterogeneity using stated-preference choice data and preference-statement data. The starting point for this analysis is the belief that the individual has preferences, and both his/her choices and preference statements are manifestations of those preferences. Our modeling contribution is linking the choice data and preference-statement data in a latent-class framework. Estimation is straightforward using the E-M algorithm, even though our model has hundreds of preference parameters. Our estimates demonstrate that: (1) within a preference class, the importance anglers associate with different Green Bay site characteristics is in accordance with their responses to the preference statements; (2) estimated across-class utility parameters for fishing Green Bay are affected by the preference-statement data; (3) estimated across-class preference-statement response probabilities are affected by the inclusion of the choice data; and (4) both data sets influence the number of classes and the probability of belonging to a class as a function of the individual’s type.

It has been widely recognized that accelerating green residential energy transition from traditional solid fuels (biomass and coal) to clean and high-efficient energy sources is critical for rural sustainable development. However, little attention has been paid to estimate panel data discrete choice models to analyze the dynamic behavior information of individual households in the process of energy transition. Hence, this paper investigates green residential energy transition using a panel dataset from 3308 rural households in eight provinces of China in 2015 and 2018. The results show that although traditional solid biomass still plays a dominating role in rural residential energy choice, fuel switching from solid fuels to modern clean energy alternatives is taking place. Off-farm employment plays an important role in the transition towards more sustainable energy sources, as households with off-farm employed heads and higher off-farm income level are highly likely to choose superior energy alternatives other than traditional biomass. Besides, the educational level of the household head and household location are also important influencing factors of household residential energy choices. Based on these findings, this paper suggests that job creation in non-farm sectors should be given a priority in future policy design to promote rural green energy transition in residential sector. North–south differences should be taken into account, whereas more policy options for optimizing biomass energy use should be explored.

Modern businesses routinely capture data on millions of observations across subjects, brand SKUs, time periods, predictor variables, and store locations, thereby generating massive high-dimensional datasets. For example, Netflix has choice data on billions of movies selected, user ratings, and geodemographic characteristics. Similar datasets emerge in retailing with potential use of RFIDs, online auctions (e.g., eBay), social networking sites (e.g., mySpace), product reviews (e.g., ePinion), customer relationship marketing, internet commerce, and mobile marketing. We envision massive databases as four-way VAST matrix arrays of Variables×Alternatives×Subjects×Time where at least one dimension is very large. Predictive choice modeling of such massive databases poses novel computational and modeling issues, and the negligence of academic research to address them will result in a disconnect from the marketing practice and an impoverishment of marketing theory. To address these issues, we discuss and identify the challenges and opportunities for both practicing and academic marketers. Thus, we offer an impetus for advancing research in this nascent area and fostering collaboration across scientific disciplines to improve the practice of marketing in information-rich environment.

A growing body of literature is exploring firm export and R&D activities. However, most studies examine the first one, whilst considering the second as an explanatory variable or vice versa. This paper contributes to this literature by exploring the joint dynamics of exports and R&D using data from a representative sample of small and medium-sized enterprises in Spanish manufacturing over the 1990-2006 period. The results confirm the existence of a strong interdependence between export and R&D activities. Indeed, engaging in export (R&D) activities will increase a firm's chances of also engaging in R&D (export) activities. This, in turn, increases firms' chances of succeeding in export (R&D) activities. Additionally, once we control for firm heterogeneity, strong persistence still remains in each activity due to true state dependence. The results are robust in the use of alternative measures of internationalization (i.e. imports) and innovative activities (product and process innovation).

This study proposes the use of semiparametric varying-coefficient methods to estimate the preference heterogeneity within stated choice data. Semiparametric varying-coefficient methods have the potential to overcome the disadvantages of conventional random parameter models and latent class models. For binary probit models with varying coefficients, in particular, this study proposes an easy-to-compute local iterative least squares (LILS) approach, based on the expectation–maximization algorithm. The finite sample properties of the LILS estimator are assessed using Monte Carlo experiments. In order to demonstrate the practical usefulness of semiparametric varying-coefficient methods, we present an empirical study, conducting an economic valuation of a landscape with dichotomous choice contingent valuations.